Do you know, what is the job of the Data Scientist?

One of the readers asked me what is the difference between Data Engineer and Data Scientist?

This world has become more data-driven and an enormous amount of data is being generated. Every object connected over IoT network generates data; when you browse any website, Google collects data for their advertisement intelligence. What not?

The responsibility of the data engineer is to transfer the data from one connected entity to another, in a more secure and reliable way.

The role of Data Scientist is to get those data, then parse it and analyze it for future development.

As per the DIKW Pyramid Model, Data Science job revolves around finding the information, knowledge from Raw Data. And it can be bundled into the stack of 4 entities:

source of data

manage and store data

analyze the data

display analyzed output (visualization, statistics)

Why is Python best Language for Data Science?

At each layer, the data scientist needs to parse and manipulate the data. For Python developer, there are various Python libraries available that make the job easy.

If you are Python developer, trust me, you are damn Lucky. Python is the best Language for Data Science. And there are various reasons.

There are so many open source data science projects available to explore in Python.

The vast number of Python Libraries can help you to play with data.

More importantly, it is one of the easiest languages to learn, even if you are a beginner.

Python Libraries for Data Science:

1. Numpy

You may be aware of one or two-dimensional data structures. It is very critical to handle multi-dimensional (N-dimensional) data. Here comes the Numpy Package. It provides numerical analysis for the multi-dimensional array.

If you have a large set of data and you want to perform some mathematical operation, what you do is running loop.

With Numpy, you don’t need to run a loop for each element. You can apply the mathematic operation on complete data set without worrying operation on each element in the dataset.

It also provides the facility to import and export data to and from external libraries using Numpy array.

Mathematics is not easy especially if I remind you about linear algebra, Fourier transform… All these operations can be done using this package. And it is very much handy for Data Analysis.

It also provides the tool for data integration with other programming languages like C/C++ and Fortran.

2. Pandas

Pandas is a Python module which makes your Data analysis job very easy. It is an open source tool that mainly focuses on the high-end data structure. It ensures faster and easy data analysis.

Many programmers (especially beginners) find it difficult to understand the Numpy package and working on the high-end data structure. To address this issue, Pandas is developed on top of Numy. So the complexity of the Numpy is cloaked behind the Pandas Python package.

If you are beginners, I would suggest using Pandas instead of Numpy package (at least to start with).

3. Matplotlib

Now you have analyzed the data? But, how will you depict it or display your analysis?

It is an open source module to display the Graphical User Interface (GUI) for your analyzed data. With this tool, you can show your pictorial data such as pie chart, bar diagram, table chart… This tool also provides the flexibility to alter and customize the image as per your requirement.

It is always easy to analyze the data from the diagram instead of going through all the numerical values and statistics (especially for the end user).

The advanced feature of this library includes zooming over the image.

After creating a pictorial diagram, you can save it in the various image format such as PDF, JPG, PNG, GIF… Saving analysis pictorial format comes handy for future reference.

4. Scipy

Scipy is the Python ecosystem or a collection of open source Python packages. As the name depicts, the packages include most of the data science related libraries and used for scientific computing.

For instance, Numpy, Pandas, and Matplotlib are already part of this ecosystem. Scipy uses Numpy array stack. Based on this array stack, it is easy to utilize various functions of Matplotlib and Pandas.

Apart from data science, it also includes the module for image processing.

5. Scikit-learn

Scikit-learn is again Python module which is built on top of the NumPy, SciPy, and Matplotlib. This module is especially known for machine learning.

There are various machine learning algorithms which are very easy to code with Scikit-learn module.

Again, it is open source. You can give it a try.

6. Anaconda

Anaconda is the Python distribution, especially build for data analysis and data science. It is open source and free to use by anyone.

This Python distribution includes all the important Python libraries you need for Data Science. If you install Anaconda on your system, you hardly need to install Python packages explicitly for Data Science.

It also comes with pip preinstalled. ( The pip is an application for managing python modules.)

Conda is package manager for Anaconda. This Python distribution comes with many preinstalled Python packages.

So, you can easily install or update or remove any module anytime in Anaconda using both pip and Conda.

7. TensorFlow

The great thing about TensorFlow is – it is built and endorsed by Google. It is an open source project for machine learning. One of the fascinating powers of this module is its power of Neural computing.

Even if you are a beginner, you can find the various TensorFlow tutorials on its official website.

As it is endorsed by Google community, you can expect the best support and future scope in Data Science for using this tool.

How to Start Exploring Python Module for Data Science?

To give a kick start learning for Data Scientist job, I would suggest you install Python on your system.

If you look at above all the Python modules for Data Science, you can clearly see; Numpy, Pandas, and Matplotlib are the main and core python modules. Based on them, other modules are developed.

For a quick start, focus on 3 things.

array objects from Numpy,

explore Pandas functionalities and

try to plot various graphs using Matplotlib.

I know, to mastering Data Science you need to explore so many python libraries. One of the biggest problems with Python is to managing dependencies among multiple Python modules.

If you don’t want to mess with your other Python work and to keep Python setup separate for Data Science, I would recommend you to create a Python virtual environment.

If you get any issue while handling Python libraries in a virtual environment, it will not hamper your existing Python environment.

So be on the safer side, use the Python virtual environment.

This is all about Python libraries for data science. It is vast and there are so many things to explore and to learn. If you have any question, I would like to discuss in the comment section. Shootout your query.

I am complete Python Nut, love Linux and vim as an editor. I hold a Master of Computer Science from NIT Trichy. I dabble in C/C++, Java too. I keep sharing my coding knowledge and my own experience on CSEstack.org portal.

Naeem Alsaadi

Caroline Kingwell

April 20, 2019 at 11:58 am

Thanks and please definitely continue spread awareness of what these packages are and their power. I’ve found that most hiring managers struggle to understand what skillsets with these packages mean with regards to similar skills like SQL queries or just even straight using pandas against their data vs less robust or slower packages/languages.

Aniruddha Chaudhari

April 20, 2019 at 12:00 pm

Thanks for your encouragement, Caroline! Very much agree with your thoughts. Python libraries are emerging and replacing many other technologies. Finding lightweight and optimistic solution requires proper skills. I have posted1 39 Python libraries that will hold 95% of Python jobs https://www.csestack.org/most-useful-python-libraries-jobs/ you might like to read it.